Compressing Aerodynamic Hazard Data

Data compression of three-dimensional computational fluid dynamics (CFD) simulation data is crucial to allow effective data-streaming for drone navigation and control. This problem is computationally challenging due to the complexity of the geometrical features present in the CFD data, and cannot be tackled by standard compression techniques such as sphere-tree. In this report, we present two different methods based on octree and cuboid primitives to compress velocity isosurfaces and volumetric data in three dimensions. Our volume compression method achieves a 1400 compression rate of raw simulation data and allows parallel computing.

Fast Data vs. Big Data With IoT Streaming Analytics and the Future Applications

Big data depicts information volume – petabytes to exabytes in organized, semi-organized, and unstructured information that can possibly be broken down for data. Fast data are facts streaming into applications and computing environments from hundreds of thousands to millions of endpoints. Fast data is totally different from big data. There is no question that we will continue generating large volumes of data, especially with the wide variety of handheld units and internet-connected devices expected to grow exponentially. Data streaming analytics is vital for disruptive applications. Streaming analytics permits the processing of terabytes of data in memory. This chapter explores fast data and big data with IoT streaming analytics.

Statistical Visualization of Big Data Through Hadoop Streaming in RStudio

Data Visualization enables visual representation of the data set for interpretation of data in a meaningful manner from human perspective. The Statistical visualization calls for various tools, algorithms and techniques that can support and render graphical modeling. This chapter shall explore on the detailed features R and RStudio. The combination of Hadoop and R for the Big Data Analytics and its data visualization shall be demonstrated through appropriate code snippets. The integration perspective of R and Hadoop is explained in detail with the help of a utility called Hadoop streaming jar. The various R packages and their integration with Hadoop operations in the R environment are explained through suitable examples. The process of data streaming is provided using different readers of Hadoop streaming package. A case based statistical project is considered in which the data set is visualized after dual execution using the Hadoop MapReduce and R script.

A Review on Big Data Stream Processing Applications: Contributions, Benefits, and Limitations

The amount of data in our world has been rapidly keep growing from time to time. In the era of big data, the efficient processing and analysis of big data using machine learning algorithm is highly required, especially when the data comes in form of streams. There is no doubt that big data has become an important source of information and knowledge in making decision process. Nevertheless, dealing with this kind of data comes with great difficulties; thus, several techniques have been used in analyzing the data in the form of streams. Many techniques have been proposed and studied to handle big data and give decisions based on off-line batch analysis. Today, we need to make a constructive decision based on online streaming data analysis. Many researchers in recent years proposed some different kind of frameworks for processing the big data streaming. In this work, we explore and present in detail some of the recent achievements in big data streaming in term of contributions, benefits, and limitations. As well as some of recent platforms suitable to be used for big data streaming analytics. Moreover, we also highlight several issues that will be faced in big data stream processing. In conclusion, it is hoped that this study will assist the researchers in choosing the best and suitable framework for big data streaming projects.

Hardware Update through Digital TV Signals

This paper presents a new hardware reconfiguration approach named hardware reconfiguration through digital television (HARD), which can update FPGA hardware modules based on digital TV (DTV) signals. Such a scheme allows several synthesized hardware cores (bitstreams) signaled and broadcast through open DTV signals via data streaming to be identified, acquired, decoded, and then used for system updates. Reconfiguration data are partitioned, encapsulated into private sections, and then sent in a carrousel fashion in order to be recovered by modified receivers. Service information content, specially designed for identifying and describing the characteristics of multiplexed hardware bitstreams, was added to the transmitted signal and provided all necessary information in the traditional DTV style. The receiver framework, in turn, checked whether those characteristics corresponded to its embedded reconfigurable devices and, if a match was found, it reassembled the related bitstreams and reconfigured the respective internal circuits. Experiments performed with an implementation of the proposed methodology confirmed its feasibility and showed that remounting and reconfiguration times were satisfactory and presented no blocking aspect. Finally, HARD can be used in several designs regarding intelligent reconfigurable devices, minimize device costs in the long term, and provide better hardware reuse.

The Metamorphosis (of RAM3S)

The real-time analysis of Big Data streams is a terrific resource for transforming data into value. For this, Big Data technologies for smart processing of massive data streams are available, but the facilities they offer are often too raw to be effectively exploited by analysts. RAM3S (Real-time Analysis of Massive MultiMedia Streams) is a framework that acts as a middleware software layer between multimedia stream analysis techniques and Big Data streaming platforms, so as to facilitate the implementation of the former on top of the latter. RAM3S has been proven helpful in simplifying the deployment of non-parallel techniques to streaming platforms, such as Apache Storm or Apache Flink. In this paper, we show how RAM3S has been updated to incorporate novel stream processing platforms, such as Apache Samza, and to be able to communicate with different message brokers, such as Apache Kafka. Abstracting from the message broker also provides us with the ability to pipeline several RAM3S instances that can, therefore, perform different processing tasks. This represents a richer model for stream analysis with respect to the one already available in the original RAM3S version. The generality of this new RAM3S version is demonstrated through experiments conducted on three different multimedia applications, proving that RAM3S is a formidable asset for enabling efficient and effective Data Mining and Machine Learning on multimedia data streams.

Pocketable Labs for Everyone: Synchronized Multi-Sensor Data Streaming and Recording on Smartphones with the Lab Streaming Layer

The streaming and recording of smartphone sensor signals is desirable for mHealth, telemedicine, environmental monitoring and other applications. Time series data gathered in these fields typically benefit from the time-synchronized integration of different sensor signals. However, solutions required for this synchronization are mostly available for stationary setups. We hope to contribute to the important emerging field of portable data acquisition by presenting open-source Android applications both for the synchronized streaming (Send-a) and recording (Record-a) of multiple sensor data streams. We validate the applications in terms of functionality, flexibility and precision in fully mobile setups and in hybrid setups combining mobile and desktop hardware. Our results show that the fully mobile solution is equivalent to well-established desktop versions. With the streaming application Send-a and the recording application Record-a, purely smartphone-based setups for mobile research and personal health settings can be realized on off-the-shelf Android devices.